Compositional Soft Prompting (CSP)

Compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) without the overhead of fine-tuning the entire model.

Reference Paper: Learning to Compose Soft Prompts for Compositional Zero-Shot Learning

Setup

conda create --name clip python=3.7
conda activate clip
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113
pip3 install ftfy regex tqdm scipy pandas
pip3 install git+https://github.com/openai/CLIP.git

Alternatively, you can use pip install -r requirements.txt to install all the dependencies.

Download Dataset

We experiment with three datasets: MIT-States, UT-Zappos, and C-GQA.

sh download_data.sh

If you already have setup the datasets, you can use symlink and ensure the following paths exist: data/<dataset> where <datasets> = {'mit-states', 'ut-zappos', 'cgqa'}.

Training

python -u train.py \
  --dataset mit-states \
  --clip_model ViT-L/14 \
  --experiment_name csp \
  --seed 0 \
  --epochs 20 \
  --lr 5e-05 \
  --attr_dropout 0.3 \
  --weight_decay 0.00001 \
  --train_batch_size 64 \
  --gradient_accumulation_steps 2 \
  --context_length 8 \
  --save_path data/model/mit-states/sample_model \
  --save_every_n 1

You can replace --dataset with {mit-states, ut-zappos, cgqa}. The best hyperparameters are included in the paper.

Evaluation

We evaluate our models in two settings: closed-world and open-world.

Closed-World Evaluation

python -u evaluate.py \
  --dataset mit-states \
  --clip_model ViT-L/14 \
  --soft_embeddings data/model/mit-states/sample_model/soft_embeddings_epoch_20.pt \
  --context_length 16 \
  --text_encoder_batch_size 36 \
  --eval_batch_size 16 \
  --experiment_name csp

You can change the experiment name to clip to benchmark the pretrained clip results.

Open-World Evaluation

For our open-world evaluation, we compute the feasbility calibration and then evaluate on the dataset.

Feasibility Calibration

We use GloVe embeddings to compute the similarities between objects and attributes. Download the GloVe embeddings in the data directory:

cd data
wget https://nlp.stanford.edu/data/glove.6B.zip

Move glove.6B.300d.txt into data/glove.6B.300d.txt.

To compute feasibility calibration for each dataset, run the following command:

python -u datasets/feasibility.py --dataset mit-states

The feasibility similarities are saved at data/feasibility_<dataset>.pt.

Evaluation

The open-world evaluation with the thresholds (feasibility calibration).

python -u evaluate.py \
  --dataset mit-states \
  --soft_embeddings data/model/mit-states/sample_model/soft_embeddings_epoch_5.pt \
  --context_length 16 \
  --text_encoder_batch_size 36 \
  --eval_batch_size 256 \
  --experiment_name czsl \
  --threshold <threshold> \
  --open_world

If <threshold> is None, then the model picks the best threshold on the validation set. We use the following thresholds:

Dataset	Threshold
mit-states	0.4069159426
ut-zappos	0.5299109123
cgqa	0.49937106273612186

Note: We use 256GB of cpu memory to evaluate cgqa.

Generalization to Higher-Order Compositions

Evaluate the trained CSP vocabulary on the new AAO-MIT-States dataset.

python aao/evaluate_att_att_obj.py \
  --experiment_name csp \
  --soft_embeddings data/model/mit-states/sample_model/soft_embeddings_epoch_20.pt

We thank Andrew Delworth and Elise Carman for helping us annotate this dataset.

Generalization to Mixed Pretrained and Fine-Tuned Vocabulary

Ablation experiment to train and evaluate CSP with reduced fine-tuned vocabulary. We run experiment on the ut-zappos dataset.

Training

python -u mix/mix_train.py \
  --dataset ut-zappos \
  --model ViT-L/14 \
  --experiment_name mix_csp \
  --seed 0 \
  --epochs 20 \
  --lr 5e-04 \
  --attr_dropout 0.2 \
  --weight_decay 0.00001 \
  --train_batch_size 64 \
  --context_length 8 \
  --save_path data/model/ut-zappos/mix_train_model_0.25 \
  --save_every_n 5 \
  --attr_keep_ratio 0.25 \
  --gradient_accumulation_steps 2

We change the --attr_keep_ratio to {0.25, 0.50, 0.75}.

Evaluation

python -u mix/evaluate_mix_train.py \
  --dataset ut-zappos \
  --soft_embeddings data/model/ut-zappos/mix_train_model_0.25/soft_embeddings.pt \
  --context_length 16 \
  --text_encoder_batch_size 36 \
  --eval_batch_size 256 \
  --experiment_name csp

Credits

The project uses openly available model, code, and datasets. Please see the credits.

Citation

If you find CSP helpful, please cite our paper:

@inproceedings{
  csp2023,
  title={Learning to Compose Soft Prompts for Compositional Zero-Shot Learning},
  author={Nihal V. Nayak and Peilin Yu and Stephen H. Bach},
  booktitle={International Conference on Learning Representations},
  year={2023}
}